Causal knowledge acquisition is crucial for various Artificial Intelligence tasks, such as causal event graph construction, reading comprehension and future event prediction. We propose an approach for acquiring causal knowledge through generating multiple plausible causes (reasons, explanations) and effects (results, consequences) for a provided input sentence. As exemplified in Figure REF , we develop two conditional decoders, one per causal direction. To train such models we mine a large-scale corpus of causal expressions from open domain web text, at a scale greatly surpassing prior work. Our goal is to generate multiple distinct possible causes and effects, where each generated sentence is not intended to be a paraphrase of other candidates. To support this output diversity when conditioned on a single shared input sentence, we turn to lexically-constrained decoding [1]}, [2]}, which allows for efficiently forcing a model to produce output containing one or more provided phrases. Our constraints are derived from a resource we construct for this work, replicating a prior effort in lexicalized causal knowledge graph construction [3]}. This graph captures causal relations as a mapping across lexical types, lemma-to-lemma, but our goal is to generate naturalistic sentences with appropriately inflected morphology: we therefore develop an approach for disjunctive positive lexical constraints, where a decoder's output must contain one of a set of provided words or phrases. In our case, these are morphological variants of the same base lemma, but our approach should benefit other applications of lexically-constrained decoding.
